System and method for employing a predictive model

ABSTRACT

A predictive insurance underwriting system. An electronic life insurance policy application processing apparatus accepts data describing a consumer. An underwriting desktop processing apparatus receives the data. An underwriting rules processing apparatus determines, based on the data describing the consumer, whether the consumer is eligible for expedited underwriting of a life insurance policy covering the consumer. If it is determined that the consumer is eligible for expedited underwriting, a tele-interview processing apparatus collects first additional data relating to the consumer during a telephonic interview and, in response to a request from a processing unit, one or more third party databases transmit second additional data relating to the consumer. The processing unit is configured to: process the data collected via the electronic life insurance policy application, and the first additional data, using a predictive model that employs machine learning algorithms and generate a first score; process the second additional data to generate a mortality risk score; if the first score at least meets a first threshold score, and the mortality risk score meets at least a second threshold score, and at least certain business rules are met, generate data describing terms of a life insurance policy. A policy issuance system offers the terms of the life insurance policy to the consumer.

CROSS REFERENCE TO RELATED APPLICATIONS

The application claims priority to U.S. Provisional Patent ApplicationNo. 62/381,702, filed Aug. 31, 2016, which is incorporated by referenceherein in its entirety.

FIELD OF THE INVENTION

The invention relates to improved methods and systems for employing apredictive model.

BRIEF SUMMARY OF THE INVENTION

The invention is directed to a system and method for employingpredictive modeling techniques. Data relating to a consumer is receivedat a computer processor. At least some of the data is collected via anelectronic application. Based on the data, it is determined whether theconsumer is eligible for expedited underwriting of a life insurancepolicy covering the consumer. If it is determined that the consumer iseligible for expedited underwriting of the life insurance policy, firstadditional data relating to the consumer is collected during atelephonic interview and second additional data relating to the consumeris collected from third party database sources. The data collected viathe electronic application and the first additional data is processedusing a predictive model that may employ machine learning algorithms anda first score is generated. The second additional data is processed togenerate a mortality risk score. If the first score at least meets afirst threshold score, and the mortality risk score meets at least asecond threshold score, which threshold may vary based on the firstscore, and at least certain business rules are met, data describingterms of a life insurance policy is generated. The terms of the lifeinsurance policy are offered to the consumer.

In connection with embodiments of the present invention, a predictiveinsurance underwriting system is employed. An electronic life insurancepolicy application processing apparatus accepts data describing aconsumer. An underwriting desktop processing apparatus receives thedata. An underwriting rules processing apparatus determines, based onthe data describing the consumer, whether the consumer is eligible forexpedited underwriting of a life insurance policy covering the consumer.If it is determined that the consumer is eligible for expeditedunderwriting, a tele-interview processing apparatus collects firstadditional data relating to the consumer during a telephonic interviewand, in response to a request from a processing unit, one or more thirdparty databases transmit second additional data relating to theconsumer. The processing unit is configured to: process the datacollected via the electronic life insurance policy application, and thefirst additional data, using a predictive model that may employ machinelearning algorithms and generate a first score; process the secondadditional data to generate a mortality risk score; if the first scoreat least meets a first threshold score, and the mortality risk scoremeets at least a second threshold score, and at least certain businessrules are met, generate data describing terms of a life insurancepolicy. A policy issuance system offers the terms of the life insurancepolicy to the consumer.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofembodiments of the invention, will be better understood when read inconjunction with the appended drawings of an exemplary embodiment. Itshould be understood, however, that the invention is not limited to theprecise arrangements and instrumentalities shown.

In the drawings:

FIG. 1 illustrates an exemplary process, and associated computercomponents, for carrying out embodiments of the present invention;

FIG. 2 illustrates an exemplary process, and associated computercomponents, for carrying out embodiments of the present invention;

FIG. 3 illustrates an exemplary process, and associated computercomponents, for carrying out embodiments of the present invention;

FIG. 4 illustrates an exemplary process, and associated computercomponents, for carrying out embodiments of the present invention;

FIG. 5 illustrates an exemplary process, and associated computercomponents, for carrying out embodiments of the present invention; and

FIG. 6 illustrates an exemplary system for carrying out aspects of thepresent invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The present invention involves a computer system that implements apredictive modeling technique. In the described exemplary embodiment,the model makes predictions about insureds under a life insurancepolicy. This results in reduced underwriting costs and cycle time forqualifying applicants. Expediting the underwriting processes decreasesthe turn-around time (from application to policy issuance) and increasesthe serviceability of the insurer. Generally, the system analyzes datagathered from a variety of sources and processes that data to identifythe applicants who qualify for the expedited process. Embodiments of thepresent invention may also result in more accurate underwriting. Moreparticularly, by developing models that consider many (e.g., thousands)of prior underwriting decision, there is a reduced chance of human biasand/or error which would produce inconsistent future underwritingdecisions.

In one embodiment, the system processes the applicant data using atleast one statistical model, which may be constructed using machinelearning algorithms. Scores are generated that are predictive of riskassociated with the applicant. Those individuals with scores exceeding adefined threshold (in combination or individually) may qualify forexpedited underwriting and policy issuance. Those applicants notqualifying for the expedited underwriting may be directed through afollow up procedure (manual or automated) or through an alternativeunderwriting approach used for applicants not subject to expeditedunderwriting, through which additional applicant data is obtained.

The mortality risk score is derived from applicant data such as, but notlimited to age, gender, state of residence, credit debt & loans,bankruptcies, employment, credit report, civil judgments, criminalhistory, and net worth data in measuring the applicant's mortality riskbased on non-medical input data. A machine-learning constructed modelanalyzes other applicant data such as, but not limited to, data providedin/from the insurance application and any digital, virtual, ortelephonic medical interview of the applicant, as well as theapplicant's medical history, prescription drug history, and motorvehicle history. While various embodiments described herein involve theuse of a tele-interview, further embodiments utilizing digital andvirtual, including virtual reality, interfaces for completion of themedical interview are contemplated, wherein the data and scores derivedtherefrom and the processing of the same correlate with those of thetele-interview as described herein. Embodiments of the present inventionthus involve compilation and storage of data from many sources. Theincreased accuracy of the process results from using two different typesof processing, and that the processing is capturing risk by usingdifferent data to predict different target variables. The model is usedto predict the underwriting decision based on mostly medical data pointsdisclosed on the application, while the other processing predictsmortality based on a set of publically available data.

The machine learning model may utilize support vector machine, or, inanother embodiment, an ensemble learning method, such as, but notlimited to, random decision forests; gradient boosting modelingtechniques and ensemble models that combine different modelingtechniques also may be utilized. Through the use of these modelingtechniques, the system is more effective at predicting whether anapplicant presents certain risks, and is able to reduce underwritingcosts and cycle time by eliminating the need for lab work and othertraditional underwriting requirements, such as the Attending PhysicianStatement (APS), for some applicants, and by gathering better applicantdata at the onset of the application which reduces attending physicianstatement orders and eliminates the need for additional applicantinterviews.

The embodiments of the present invention comprise or are enabled througha computer-implemented administrative and/or processing system that maybe executed in a client/server environment, cloud based environment,mobile device, etc., having hardware and software components. Suchsystems and devices may utilize any native application languages, anyartificial intelligence languages, any communication and processinglanguage, machine-to-machine protocols, and/or other operation,communication, and processing techniques. Database server(s) may includea database services management application that manages storage andretrieval of data from the database(s) or data repositories. Thedatabases may be relational or unstructured databases; however, otherdata organizational structures may be used without departing from thescope of the present invention.

Thus, in embodiments of the present invention, database(s)/datarepositories maintained by the systems of the provider transacting asdescribed herein (e.g., the insurance company) can be used for storingdata describing the insurance or financial instruments described herein,and associated accounts. Such information may be stored as part of arecord for an applicant.

Reviewing an exemplary embodiment of the inventive process in moredetail, an agent may submit an electronic life insurance application onbehalf of a consumer. Then, a telephonic or digital interview may beconducted with the consumer to ask a series of medical questions. Apredictive model is used to analyze the data from the application andinterview, and output a score. Depending on the score, an assessment maybe made as to whether there is a high probability of the applicant beinglow risk, in which case no additional information (e.g., lab work) isneeded. Otherwise, additional information (e.g., that is provided by labwork) is needed to assist in making a decision.

With reference to FIG. 1, an exemplary embodiment of the process andassociated computer architecture is illustrated. Via electronicapplication interface 101 (e.g., electronic life insurance policyapplication processing apparatus), in step 111, a first part of anapplication (e.g., Part A), with a new application type code (e.g.,“Tele App”) and an indication that the consumer has agreed to, and theapplication qualifies for, completion of the remainder of theapplication via a telephonic interview, is provided to the systems of aninsurance company associated with New Business. In step 112, the newapplication is assigned a policy number and is submitted via WebServices to underwriting desktop 105. In step 113, certain reports areobtained in accordance with underwriting requirements, e.g., a MotorVehicle Report (MVR) from the state in which the policy is submitted, areport from the Medical Information Bureau (MIB), and a PrescriptionDrug History (RX). In step 114, a telephonic interview is conducted tosecure the remaining information (Part B of the application) necessaryto complete the application. In step 115, the data from Part A and PartB of the application is submitted to the underwriting rules engine 102.In step 116, the document containing the data from the telephonicinterview, plus other supporting documentation, e.g., obtained fromthird party database sources, are saved in electronic file storage 107(also referred to herein as a virtual file cabinet), preferably in PDFformat. In step 117, a request is made for the underwriting risk score.In step 118, the data from Part A and Part B of the application istransmitted to the underwriting risk model scoring engine 103 (i.e., aproprietary predictive model) for processing. The details of thisprocessing are described with reference to FIG. 3. In step 119, theunderwriting risk model scoring engine 103 returns a risk score. In step1110, additional data may be pulled and is processed using processor 104to determine a mortality risk score. In step 1111, the data collectedfrom Part A and Part B of the application, along with the mortality riskscore generated using processor 104, is returned. In step 1112, thisdata is submitted via Web Services to underwriting desktop 105. Notethat the underwriting desk top 105 may receive the requirements atdifferent times. Such data is stored in database 106. Based on all ofthe collected data and accompanying scores, the underwriting desktop 105sends the consumer status information, i.e., informing the consumer hisrate and offered policy terms, or whether additional lab work isrequired. In a preferred embodiment, such information is sent to theproducer for conveying to the consumer, rather than to the consumerdirectly from the insurance company.

With reference to FIG. 2, the input to the mortality-risk basedprocessor 104 includes data included in Parts A and B of the application(e.g., through an electronic application and a tele-interview asdescribed with reference to FIG. 1), as well as data from RX, MVR andMIB reports for a given consumer. The social security number obtained inthis step is used to search public data records for informationpertaining to the consumer (e.g., credit score, driving records). Thisdata is processed using processor 104. The other data, i.e., thatobtained via Parts A and B of the application, is translated to astandard format. After translation, this data is inputted into theunderwriting risk model scoring engine 103, which executes the modelthat predicts risk across a known spectrum. For instance, while anynumber of risk levels may be defined across a spectrum, the underwritingrisk model scoring engine 103 may predict whether a case falls withinany of the following risk levels: Preferred Plus Non-Tobacco, PreferredNon-Tobacco, Preferred Tobacco, Standard Non-Tobacco, Standard Tobacco,Rated Non-Tobacco, Rated Tobacco, and Decline. In one embodiment, theunderwriting risk model scoring engine 103 executes the model to predictif the case will be preferred or not (i.e., the score can be interpretedas the percent chance of being preferred). A score is calculated. Thedata output from model 103 and mortality-risk based processor 104 isthen processed using business rules processing engine 108. Thesebusiness rules provide for an additional risk screen on top of the modeland other processing. Thus, the present invention allows for combining amodel with mortality risk based processing with a series of rules tosegment low and high risk applicants. The rules flag certain high riskcases (e.g., high blood pressure, high cholesterol) for fullunderwriting, regardless of their score. The output of the businessrules engine 108 indicates whether the consumer qualifies foraccelerated underwriting (in which a rate and policy terms may beprovided to the consumer) or whether additional underwriting isrequired, in which case the consumer will need to have lab workperformed.

With reference to FIG. 3, the electronic application/Part A submissionportion of the process is illustrated in greater detail. In step 301, anagent accesses the electronic application process, and selects theissuance state and product type. In step 302, the agent enters the dateof birth, face amount of the policy desired, and height/weight of theconsumer in the application set up screen. In step 303, the applicationset up screen refreshes and the logic determines, based on theinformation input to date, if the application is suitable for completingthe application via telephonic interview, should be processed throughthe traditional channel, or whether the consumer has an option to choosefor the application to be subject to a telephonic interview, or proceedthrough the traditional channel. In some instances, in step 304, if theparameters are invalid, a screen will be displayed to the agentindicating that the consumer does not qualify for the life insuranceproduct. In other instances, the data will indicate that acceleratedunderwriting is available. In this instance, in step 305, the screenwill display a message indicating that the application qualifies foraccelerated/express issue processing. In step 306, the agent completesthe express application and submission process. If in step 303 theparameters indicate that there is a choice, in step 307, the screen willdisplay a message indicating that the consumer may qualify for thetele-application process. Here, the consumer may indicate he wants toproceed with the tele-application process or a traditional process. Ifthe consumer selects the tradition process, Parts A and B of theapplication are completed in the traditional way and submitted, in step312. If the consumer indicates his desire to proceed with thetele-application process, in step 308, the application set up screenpopulates the tele-application process stipulations (e.g., onlyeSignatures will be accepted, certain riders may not be available, etc.)and an additional check box asking for confirmation that the consumerwishes to use the tele-application process is checked. In step 309, theagent asks the consumer questions, inputs responses into the electronicapplication on behalf of the consumer and, upon completion, clicks thebutton to complete the process. If the agent does not click the button,the process proceeds to a traditional process in step 312. If the agentclicks the button in step 309, the “next” button on the screen is madeavailable to the agent in step 310. Upon clicking the “next” button, instep 313, the agent submits the information required by the screensrepresenting Part A of the application, and the electronic applicationgenerates new Part A paperwork in the background. In step 314, a screenis displayed indicating a question to the consumer regarding how hewould like to complete his tele-interview—e.g., call the centerimmediately after signing the application or schedule a date, time, andlanguage for a call to the consumer to complete the application. In step315, the consumer's choice is reviewed. If the consumer chooses tocomplete the tele-interview immediately, in step 316, the screenrefreshes with a message indicating that the form should be signed andsubmitted, and then the agent should call that results in routing theconsumer to a medical professional to conduct the interview. A signatureis then obtained in step 317 (eSignature or face-to-face). Theapplication is then submitted and, in step 318, a reminder message isdisplayed to complete Part B of the application.

If the consumer decides in step 315 to schedule the tele-interviewlater, the process moves to a sub-process to allow for scheduling thetele-interview. Here, in step 319, the electronic-application providerpasses a unique identifier and consumer information to the providerperforming the telephonic interview services. In step 320, the schedulerwindow is generated and pre-populated information appears on theschedule screen. In step 321, the provider of the telephonic interviewservices provides date/time and language information for the consumerand the agent confirms the schedule. In step 322, the scheduleinformation and confirmation are sent back to the service supporting theelectronic application process. In step 324, an email confirmation ofthe scheduled telephonic interview is sent to the consumer. In step 323,the window closes and the schedule time/date is recorded on the PDFversion of the application. In step 325, the process returns to theelectronic application process (i.e., step 317).

In still further instances, in step 303, the parameters indicate thatthe tele-application process is not available. In this instance, thescreen displays a message indicating that the application does notqualify for the tele-application process and Parts A and B of theapplication are completed in the traditional way and submitted, in step312.

Referring now to FIG. 4, the data collected from the electronicapplication process described with reference to FIG. 3 is then submittedto the underwriting desktop 105. In step 401, the policy number andapplication type are assigned, and requirements for the policy aregenerated (i.e., based on the age and face of the policy, the list ofunderwriting requirements for the application is generated). In step402, the case is assigned a pending status, assuming the agent/advisoris properly licensed to sell the applicable products. In step 403, theunderwriting rules engine 102 receives a new business feed ofapplication data. In step 404, requirements for this type of application(e.g., RX, MIB and MVR) are ordered and a call to the tele-interviewsystem 407 is made. In step 405, the underwriting desktop 105 refresheswith auto-documentation from Part A of the application, MIB, MVR and RX.In step 406, the results of Part B of the application from thetele-interview are received, as well as documentation of Part Aapplication, MIB, MVR and RX. In step 408, the underwriting rules forthe insurance company are run against the data. If, based on this data,the consumer is not eligible for accelerated underwriting, the processmoves to step 409, in which orders for lab work are sent to theunderwriting desktop 105. In step 410, the underwriting desktopgenerates the orders for the lab work and indicates the need forunderwriter action. If, based on the data, it is determined that theconsumer is eligible for accelerated underwriting in step 408, in step411, the underwriting rules engine 102 sends an initial reviewrequirement to the underwriting desktop 105 and, in step 412, theunderwriting desktop 105 populates the initial review requirement (i.e.,to determine if all the requirements for input data are present).

Referring back to step 401, after a policy number is assigned, in step413, the virtual file cabinet 107 receives images, creates a newbusiness application transaction and assigns a new indicator to theassociated documentation. In step 414, the electronically submittedtele-application is assigned to a queue for processing. In step 415, thecase is assigned to a new business team for processing. In step 416, thenew business case manager looks at the case and checks that requirementsare met, the case is in good order (i.e., no missed data points) andthat the agent is licensed to sell the particular product. In step 417,underwriting performs initial review. In step 418, follow up isconducted with an agent if the tele-application is not complete. In step419, the completed tele-application is saved in the virtual file cabinet107. In step 420, a call is made to the tele-interview system 109 (e.g.,tele-interview processing apparatus) by the new business team (i.e., tostart the phone interview with the applicant to complete Part B of theapplication). Contact is made with the consumer in step 421 (via anincoming call or outbound call). In step 422, updated comments obtainedduring the call are sent to the underwriting desktop 105. In step 423,the interview is initiated—questions are asked and answered. In step424, certain answers to questions may require follow up questions and/orreconciliation between answers provided by the consumer during thetele-interview and RX, MVR, and MIB. At the completion of the interview,the client authorizes a voice signature and the call ends at step 425.As before, a copy of the completed interview responses is saved invirtual file cabinet 107, in step 419. At this point the processcontinues, with reference to FIG. 5.

Referring now to FIG. 5, in step 501, calls are made by the underwritingrules engine 108 to secure scores from the underwriting risk modelscoring engine 103 and resulting from the mortality-risk processor 104.Underwriting risk model scoring engine 103 receives the data from PartsA and B of the application, as well as RX, MVR and MIB. Demographicinformation about the consumer is obtained from third party sources. Instep 503, a mortality risk score is generated, as well as a documentshowing the data used in connection with calculating the score. In step505, underwriting risk model scoring engine 103 generates a score. Instep 506, the underwriting rules engine 108 receives the scores and runsunderwriting rules. In step 517, the underwriting desktop 105 receivesdocumentation on Part B of the application (Part A having been receivedpreviously), the scores and the underlying data relied upon by the model103 and processor 104 to generate the scores. In step 507, it isdetermined if both of the consumer's scores are acceptable (e.g., meet acertain minimum threshold) and no knock-out underwriting rules have beentriggered. If either the scores are not acceptable or knock-out ruleshave been triggered, the consumer is not eligible for acceleratedunderwriting and, in step 508, orders for lab work are sent, as well asa call for underwriter action to the underwriting desktop 105.

In some embodiments, the thresholds to which the scores are comparedvary based on the scores (e.g., the higher the score generated byunderwriting risk model scoring engine 103, the lower the threshold forthe mortality risk score). Further, in another embodiment, theunderwriting rules engine 108 utilizes a score-based matrix thatevaluates, in unison, the score received from the underwriting riskmodel scoring engine 103 and score received from the mortality-riskprocessor 104, in view of the scores' respective minimum thresholds, tomake a pass/fail determination of the applicant's eligibility forexpedited underwriting, subject to the triggering of the knock-outrules. In another embodiment, the model offers additional paths beyondthe binary pass/fail determination that simply either advances the casefor accelerated underwriting (step 509) or orders lab work and furtherunderwriting (step 508). The additional paths enable other potentialoutcomes, enhancing the overall underwriting process. By way of example,in cases where the insurer is highly likely to decline based on theinformation received, the underwriting rules engine 108 could instructthe insurer's underwriter to decline the case immediately, subject todefined confirmations, saving time and effort for both the applicant andthe insurer and avoiding the cost of obtaining lab work and/or an APS.Conversely, there may be a limited group of cases where applicants arelikely to be highly rated based on the information received, e.g.,Preferred Plus. In these instances, the insurer may make an immediatedetermination on the applicants' eligibility and offer coverage, ratherthan spending greater time and resources obtaining additionalinformation.

In step 512, the underwriting desktop 105 sends requirement orders tothe system that manages the order of the third party underwritingrequirements and populates the underwriter action requirements (i.e.,actions that underwriting would be required to follow based on the ageand face of the policy, and based on the data regarding the applicantthat has been received through Parts A and B of the application). Instep 513, lab work follow up is completed, and additional requirementsare ordered in order to make an underwriting decision. If the scoresmeet the required threshold and no knock-out rules are triggered in step509, the underwriting rules engine 108 sends the auto decisionrequirement to the underwriting desktop 105. If the conditions are notmet, then decision review will occur (i.e., underwriting will review thedata and rules and make an underwriting decision). In step 510, theunderwriting desktop 105 populates the decision review or auto decisionrequirement. In step 511, an automated approval email is sent to theagent and case contact. In step 515, typical new business applicationwork necessary to issue the policy as an approved policy is conducted.In step 516, in some embodiments, documentation is generated if requiredfor reinsurance of accelerated underwriting cases. In step 514, thepolicy is issued by a policy issuance system.

As referenced above, embodiments may process applicant data usingstatistical models constructed using machine learning algorithms togenerate scores predictive of risk associated with the applicant.Machine learning may be a preferred approach where predictive accuracyis paramount. While machine learning models tend to reveal limitedinsight into relationships between variables, are often very complex(e.g., may contain thousands of trees in a boosted model), and oftenforego any assumptions or requirements about normality (e.g.,well-behaved, bell-shaped data) how variables may interact, such modelstend to be helpful in analyzing large datasets. For example, machinelearning can be very effective in accurately asserting and assessingassumptions in analyzing a high number of variables, e.g., in oneexemplary embodiment over 1300 variables relating to medical conditionsand groupings are effectively analyzed.

Other embodiments utilize deep learning or other learning or statisticalmodeling constructions outside of machine learning. By way of exampleand not limitation, the following modeling domains may be utilized,independently or in any combination, in accordance with embodiments ofthe present invention in constructing models to generate scorespredictive of risk associated with the applicant: data science,knowledge discovery in databases (KDD), deep learning, data mining,machine learning, artificial intelligence, pattern recognition, neurocomputing, data visualization, math and statistics, database technology,substantive domain knowledge.

In one exemplary embodiment, penalized regression models (elastic net)are built through a statistical learning modeling domain and utilized togenerate risk-predictive scores. Statistical learning, in contrast withmachine learning, focuses on establishing mathematical equations thatmap to the problem being solved in terms of the input variables byyielding standard coefficients that help provide more explanatory powerwith respect to the variables. This explanatory power offered throughstatistical learning may, in some circumstances, come at a trade off inthe predictive power generally provided through machine learning. In oneembodiment, statistical learning techniques are used in analyzingapplicant responses to non-medical condition questions addressing, forexample, approximately sixty-five variables for attributes likeapplicant's age, BMI, work status, tobacco use, specified amount, etc.In exemplary embodiments as such where there are a more limited numberof variables analyzed, e.g., only approximately sixty-five variables,the insurer's ability to understand or explain the prophylactic value ofthe questions presented to the applicant in terms of risk may be moreintuitive and easier to understand based on historical data, and can bemore easily corroborated via traditional actuarial approaches.

Referring now to details of the underwriting risk model scoring engine103 more specifically, this proprietary model accepts as its inputs aplurality of variables. The model then returns a score which isapproximately representative of the chance of an application beingpreferred for a life insurance policy. This output is used as one of thethree factors to determine if a case should be accelerated forunderwriting purposes, as described in detail elsewhere herein (i.e.,the applicant must meet the minimum score defined for both models andnot have any knock out rules flagged).

Referring now to how this model may receive data required to generate ascore, an XML transaction file may be sent to the model for purposes ofdata input. In this XML transaction file, the same set of lines repeat,with the variable names following the “KeyName=”. In this example, thisstructure allows a number of variables to be passed through. It will beunderstood by those skilled in the art that other numbers of variablesmay be used within the scope of the present invention. In otherpreferred embodiments, however, an undetermined number of variables maybe accepted for input, rather than a fixed set of variables, whichallows for more flexibility in the model. More particularly, thedetermination of the number of variables that are passed through themodel may vary by applicant, e.g., depending on the number of positiveresponses to the telephone interview regarding health conditions.Responses received by way of the telephone interview allows for a moreexpansive list of variables to be taken into consideration during theanalysis (e.g., over 1,000 specific health conditions could beevaluated, although far fewer conditions are likely to be relevant toany particular applicant). Allowing for input of this granular level ofdetail into specific conditions an applicant may have enhances the levelof predictiveness of the model. One manner of accomplishing this is toextend the length of the XML file to send the additional data pointsneeded for the model to run. It can be structured such that only theconditions that have been identified will be sent in the XML. It isassumed that each condition is false unless data is received statingthat it is true. Thus, only the existing conditions need to be sent,instead of sending over 1000 true/false variables in the XML.

In some embodiments, the methods are carried out by a system thatemploys a client/server architecture such as, for example, thecollection of components illustrated and described with reference toFIGS. 1-5. Such exemplary embodiments are described as follows withreference to FIG. 6. The data that may be used as an input to thesystem, and the outputs from the system, may be stored in one or moredatabases 601. Database server(s) 602 may include a database servicesmanagement application 603 that manages storage and retrieval of datafrom the database(s) 601. The databases 601 may be relational databases;however, other data organizational structure may be used withoutdeparting from the scope of the present invention.

One or more application server(s) 604 are in communication with thedatabase server 602. The application server 604 communicates requestsfor data to the database server 602. The database server 602 retrievesthe requested data. The application server 604 may also send data to thedatabase server 602 for storage in the database(s) 601. The applicationserver 604 comprises one or more processors 605, non-transitory computerreadable storage media 607 that store programs (computer readableinstructions) for execution by the processor(s), and an interface 606between the processor(s) 605 and computer readable storage media 607.The application server 604 may store the computer programs referred toherein.

To the extent data and information is communicated over a network (e.g.,the Internet or an Intranet), one or more network servers 608 may beemployed. The network server 608 also comprises one or more processors609, computer readable storage media 611 that store programs (computerreadable instructions) for execution by the processor(s), and aninterface 610 between the processor(s) 609 and computer readable storagemedia 611. The network server 608 is employed to deliver content thatcan be accessed through the communications network 612, e.g., by an enduser employing computing device 613. When data is requested through anapplication, such as an Internet browser, the network server 608receives and processes the request. The network server 608 sends thedata or application requested along with user interface instructions fordisplaying a user interface on device 613.

The computers referenced herein are specially programmed to perform thefunctionality described herein.

The non-transitory computer readable storage media (e.g., 607 or 611)that store the programs (i.e., software modules comprising computerreadable instructions) may include volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information such as computer-readable instructions, datastructures, program modules, or other data. Computer readable storagemedia may include, but is not limited to, RAM, ROM, ErasableProgrammable ROM (EPROM), Electrically Erasable Programmable ROM(EEPROM), flash memory or other solid state memory technology, CD-ROM,digital versatile disks (DVD), or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer system andprocessed.

It will be appreciated by those skilled in the art that changes could bemade to the exemplary embodiments shown and described above withoutdeparting from the broad inventive concept thereof. It is understood,therefore, that this invention is not limited to the exemplaryembodiments shown and described, but it is intended to covermodifications within the spirit and scope of the present invention asdefined by the claims. For example, specific features of the exemplaryembodiments may or may not be part of the claimed invention and featuresof the disclosed embodiments may be combined. Unless specifically setforth herein, the terms “a”, “an” and “the” are not limited to oneelement but instead should be read as meaning “at least one”.

It is to be understood that at least some of the figures anddescriptions of the invention have been simplified to focus on elementsthat are relevant for a clear understanding of the invention, whileeliminating, for purposes of clarity, other elements that those ofordinary skill in the art will appreciate may also comprise a portion ofthe invention. However, because such elements are well known in the art,and because they do not necessarily facilitate a better understanding ofthe invention, a description of such elements is not provided herein.

Further, to the extent that the method does not rely on the particularorder of steps set forth herein, the particular order of the stepsshould not be construed as limitation on the claims. The claims directedto the method of the present invention should not be limited to theperformance of their steps in the order written, and one skilled in theart can readily appreciate that the steps may be varied and still remainwithin the spirit and scope of the present invention.

What is claimed is:
 1. A predictive assessment system comprising: anapplication processing apparatus that receives from a first userinterface, input data describing a consumer, wherein the applicationprocessing apparatus stores the input data in an electronic file storageand assigns a transaction indicator to the input data, the input databeing a first format; a tele-interview electronic device, that receivesfirst additional data relating to the consumer, the tele-interviewelectronic device configured to generate a scheduler window on the firstuser interface and configured to populate on the scheduler window atleast some of the input data received from the electronic file storageand some of the first additional data, wherein the tele-interviewelectronic device stores the first additional data in the electronicfile storage and assigns the transaction indicator to the firstadditional data, the first additional data being in a second format; oneor more third party data bases that transmit, in response to a requestfrom a processing unit, second additional data relating to the consumerif the consumer is eligible for participation in an automated predictiveprocess, the second additional data being in a third format and obtainedfrom publicly available third-party sources, stored in the electronicfile storage, and assigned the transaction indicator; the processingunit having program instructions configured to: map the input data tothe first additional data and the second additional data based on thetransaction indicator; convert input data describing the consumer fromthe first format to a fourth format, the first additional data from thesecond format to the fourth format, and the second additional data fromthe third format to the fourth format, wherein the first format, thesecond format, and the third format are different than the fourthformat; generate, using a predictive model constructed via machinelearning trained by analyzing one or more of medical data, insurancedata, and motor vehicle history data, a risk score associated with theconsumer based on the converted input data and the converted firstadditional data; generate, using the predictive model, a mortality scoreassociated with the consumer based on the converted second additionaldata; determine that the risk score exceeds a first threshold score, andthe mortality score exceeds a second threshold score, and automaticallygenerate a prediction assessment, in response to the risk scoreexceeding the first threshold score and the mortality score exceedingthe second threshold score, wherein the prediction assessment includesan indication that the consumer is low risk and eligible to receive anautomated insurance policy offer via the automated predictive processwithout further action from an underwriter, wherein the processing unitutilizes a score-based matrix to determine if the risk score at leastmeets the first threshold score, and the mortality score at least meetsthe second threshold score; an issuance system that receives, from theprocessing unit, the prediction assessment, and automatically deliversthe automated insurance policy offer based on the prediction assessment,wherein the issuance system causes the automated insurance policy offerto be displayed on a second user interface to indicate completion of theautomated underwriting process.
 2. The system of claim 1, wherein therisk score and the mortality score are predictive of a risk associatedwith the consumer.
 3. The system of claim 1, wherein the secondthreshold score is dependent on the risk score.
 4. The system of claim1, wherein the second additional data includes a social security numberof the consumer.
 5. The system of claim 1, further comprising if atleast certain business rules are not met, generating a request for labwork data associated with the consumer.
 6. The system of claim 1,further comprising if the risk score does not at least meet the firstthreshold score, generating a request for consumer lab work data.
 7. Thesystem of claim 1, further comprising if the mortality score does not atleast meet the second threshold score, generating a request for consumerlab work data.
 8. The system of claim 1, wherein the first userinterface and the second user interface are the same.
 9. The system ofclaim 1, wherein the scheduler window is populated with at least some ofthe input data and some of the first additional data based on a uniqueidentifier associated with consumer, the unique identifier beingreceived by the tele-interview electronic device.
 10. The system ofclaim 1, wherein the second threshold score is inversely related to therisk score.